Instructions to use BEE-spoke-data/tiny-random-MPNetForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BEE-spoke-data/tiny-random-MPNetForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BEE-spoke-data/tiny-random-MPNetForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/tiny-random-MPNetForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("BEE-spoke-data/tiny-random-MPNetForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4817d0c96a3fa30894840585fbe148a7cd50c5aa551dda3bd5c67b4a5119466
- Size of remote file:
- 956 kB
- SHA256:
- 305c78e6afe14baab0f1aa94191ed0e6a853805bbe0901f3c9dd5abfe2486f0d
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